Open-ended Mechatronics Projects Boost Higher Order Thinking

A semester-long mechatronics project asked students to decide which of two automatic control approaches was better for a ground vehicle leader-follower task, without being given explicit performance criteria or test procedures. Throughout the course, labs taught the technical building blocks: sensors, actuators, programming both proportional-integral-derivative and fuzzy controllers, and using computer vision for object detection. For the final project students defined their own evaluation metrics and experiments, then compared the approaches to make a design decision. Implemented over three semesters, the project led students to appreciate tackling open-ended, real-world problems, to create creative evaluation methods, and to notice how many factors affect comparative studies, demonstrating higher order thinking skills.

What the study examined

This work explored whether a course project could push students toward higher order thinking — the analysis, synthesis, and evaluation levels of Bloom’s Taxonomy — in a mechatronics class. Instead of giving a single prescribed procedure or fixed grading rubric, instructors asked teams to choose which of two automatic control approaches would better produce leader-follower behavior for a ground vehicle. Students were not provided explicit performance criteria or step-by-step experimental plans; they had to design those themselves.

Across the semester, lab exercises built the core skills needed for the capstone task. These labs covered sensors and actuators, controller programming (including proportional-integral-derivative and a rule-based alternative), and computer vision for detecting the lead vehicle’s signature. The final assignment required students to combine these elements into their own comparative study and reach a reasoned decision about which control approach to use.

Key findings

When the approach was used over multiple semesters, students responded positively to working on an open-ended, realistic problem rather than following fixed instructions. Teams developed original performance criteria and inventive experimental methods to compare the two control approaches, showing the targeted higher order thinking skills.

Students also learned that running a fair comparison is challenging: many interacting factors influenced performance, so arriving at a clear winner required careful analysis and consideration. The project pushed learners beyond implementing controllers to evaluating trade-offs and justifying design choices.

Why it matters

Engineering practice often involves ambiguous problems and decisions without a single right answer. By asking students to define what “better” means and to design experiments that support their decision, the course created conditions similar to professional work and encouraged deeper cognitive processes than routine, prescriptive tasks.

Educators seeking to cultivate analysis, synthesis, and evaluation skills can use this model to transform hands-on coursework into experiences where students must think like designers and evaluators — creating metrics, running tests, and weighing evidence to reach defensible conclusions.